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Sci Rep. 2019 Dec 27;9(1):20038. doi: 10.1038/s41598-019-56527-3.

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data.

Author information

1
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. aleksei.tiulpin@oulu.fi.
2
Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland. aleksei.tiulpin@oulu.fi.
3
Biomedical Imaging Group Rotterdam, Depts. of Medical Informatics & Radiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
4
Department of General Practice, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
5
Department of Orthopedics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
6
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
7
Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
8
Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
9
Department of Radiology & Nuclear Medicine, University Medical Center Rotterdam, Rotterdam, The Netherlands.
10
Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.

Abstract

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilises raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalised therapeutic plans.

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